An ecological index that is constructed based on remote sensing images can enable a rapid evaluation of the quality of the urban ecological environment and can provide a scientific basis for the construction of urban ecological civilization. Taking Meizhou City, in Guangdong in China, as a study area, based on the Landsat TM/OLI and MODIS remote sensing data, this paper extracts the total primary productivity (GPP), land surface temperature (LST), humidity component (Wetness), and bare soil index (SI), which represent the remote sensing ecological index (RSEI) evaluation indicators. The greenness, heat, humidity, and dryness in the indicators are used to characterize the quality of the regional ecological environment and its change characteristics. The results show that: (1) the high-quality areas of ecological environment in the study area are increasing, and the proportion of high-grade RSEI areas has increased from 61.7% to 66.2%. (2) About 26.3% of the area in the study area has an optimized ecological environment quality. (3) The correlation between POI and each index is significant, among which GPP and LST have a high correlation with RSEI, while POI and RSEI have a moderately negative correlation. (4) MODIS data are suitable for regional ecological environmental quality assessments. In the future, research on RSEI data sources and processing efficiency and the spatiotemporal changes of ecological quality and environmental factors can be strengthened, and the sustainable development of ecological protection and urban construction planning can be explored.

Based on the equivalent factor table of terrestrial ESV in China in 2010, this paper proposes a regionalized and interannual ESV correction method. Taking southern Guangzhou as the study area, which is located in the geometric center of the Guangdong–Hong Kong–Macao Greater Bay Area (GHM GBA), this study evaluated the ecosystem service equivalent value (ESEV) and ecosystem service value (ESV) and their changes in 2004, 2010 and 2016 and carried out grid processing, trend analysis and global/local spatial autocorrelation of the ecosystem service density (ESD) in the study area by using the Natural Resources Integrated Database of Guangdong Province (NRIDB). The results of data processing and spatial analysis computed in this study were published as Web services through the NRIDB and displayed in the visualization front-end. The results showed that (1) during 2004–2016, the urbanization process in the study area led to the expansion of built-up land, which occupied a large amount of ecological land and resulted in a decreasing trend of ESV. (2) Benefiting from socioeconomic development, the standard value volume of the ESEV (D value) kept increasing, which resulted in an increasing trend for the total ESV. (3) The spatial autocorrelation analysis results of ESD indicated that the spatial distribution of ESD in the study area had strong spatial agglomeration and positive spatial correlation. With the progress of urbanization and increases in research scale and interval distance, the spatial difference in ESD became larger over time. The research results provide a reference for the coordinated allocation of natural resources and environmental protection in the GHM GBA.